Realtime Covid-19 Dataset Analysis

import pandas as pd
import numpy as np
import plotly.offline as pyo
import plotly.graph_objects as go
import plotly.express as px
import matplotlib.pyplot as plt

pyo.init_notebook_mode()

Assigning the links for various covid-19 datasets

confirmed_cases = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
death_cases = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
recovery_cases = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
country_cases = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_country.csv"
country_cumulative = "https://covid19.who.int/WHO-COVID-19-global-table-data.csv"

Creating the dataframes of the datasets and displaying dimension of each dataset

confirmed_df = pd.read_csv(confirmed_cases)
print(confirmed_df.shape)
deaths_df = pd.read_csv(death_cases)
print(deaths_df.shape)
recovery_df = pd.read_csv(recovery_cases)
print(recovery_df.shape)
cases_country_df = pd.read_csv(country_cases)
print(cases_country_df.shape)
country_cumulative_df = pd.read_csv(country_cumulative)
print(country_cumulative_df.shape)

(275, 479)
(275, 479)
(260, 479)
(192, 14)
(238, 13)

The first 5 records of the confirmed cases dataframe

confirmed_df.head()

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	5/1/21	5/2/21	5/3/21	5/4/21	5/5/21	5/6/21	5/7/21	5/8/21	5/9/21	5/10/21
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	...	59939	60122	60300	60563	60797	61162	61455	61755	61842	62063
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	...	131185	131238	131276	131327	131419	131510	131577	131666	131723	131753
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	...	122311	122522	122717	122999	123272	123473	123692	123900	124104	124288
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	...	13232	13282	13295	13316	13340	13363	13390	13406	13423	13429
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	...	26815	26993	27133	27284	27529	27921	28201	28477	28740	28875

5 rows × 479 columns

cases_country_df.columns

Index(['Country_Region', 'Last_Update', 'Lat', 'Long_', 'Confirmed', 'Deaths',
       'Recovered', 'Active', 'Incident_Rate', 'People_Tested',
       'People_Hospitalized', 'Mortality_Rate', 'UID', 'ISO3'],
      dtype='object')

Total number of records in the confirmed cases dataframe

confirmed_df['Country/Region'].nunique()

192

confirmed_df = confirmed_df.replace(np.nan, '', regex=True)
deaths_df = deaths_df.replace(np.nan, '', regex=True)
recovery_df = recovery_df.replace(np.nan, '', regex=True)
cases_country_df = cases_country_df.replace(np.nan, '', regex=True)
country_cumulative_df = country_cumulative_df.replace(np.nan, '', regex=True)

global_data = cases_country_df.copy().drop(['Lat','Long_','Country_Region','Last_Update','Mortality_Rate','UID','ISO3','People_Tested','People_Hospitalized'], axis = 1)
global_summary = pd.DataFrame(global_data.sum()).transpose()

global_summary

	Confirmed	Deaths
0	158969512.0	3304132.0

confirmed_ts = confirmed_df.copy().drop(['Lat','Long','Country/Region','Province/State'],axis=1)
confirmed_ts_summary = confirmed_ts.sum()

confirmed_ts_summary

1/22/20          557
1/23/20          655
1/24/20          941
1/25/20         1433
1/26/20         2118
             ...    
5/6/21     156070356
5/7/21     156901680
5/8/21     157688226
5/9/21     158326318
5/10/21    158951027
Length: 475, dtype: int64

Confirmed Covid-19 Cases Vs Time (Global)

fig1 = go.Figure(data=go.Scatter(x=confirmed_ts_summary.index, y = confirmed_ts_summary.values, mode='lines+markers'))
fig1.update_layout(title='Total CoronaVirus Confirmed Cases (Globally)', yaxis_title='Confirmed Cases', xaxis_tickangle=315)
fig1.show()

color_arr = px.colors.qualitative.Dark24

def draw_plot(ts_array, ts_label, title, colors, mode_size, line_size, x_axis_title, y_axis_title, tickangle=0, y_axis_type='', additional_annotations = []):
    fig = go.Figure()
    
    for index, ts in enumerate(ts_array):
        fig.add_trace(go.Scatter(x=ts.index,
                                y=ts.values,
                                name=ts_label[index],
                                line=dict(color=colors[index], width=line_size[index]),connectgaps=True))
    
    x_axis_dict = dict(showline=True,
                      showgrid=True,
                      showticklabels=True,
                      linecolor='rgb(204,204,204)',
                      linewidth=2,
                      ticks='outside',
                      tickfont=dict(family='Arial',size=12,color='rgb(82,82,82)'))
    if x_axis_title:
        x_axis_dict['title'] = x_axis_title

    if tickangle > 0:
        x_axis_dict['tickangle'] = tickangle

    y_axis_dict = dict(showline=True,
                      showgrid=True,
                      showticklabels=True,
                      linecolor='rgb(204,204,204)',
                      linewidth=2,)
    if y_axis_type := "":
        y_axis_dict['type'] = y_axis_type
    
    if y_axis_title:
        y_axis_dict['title'] = y_axis_title

    fig.update_layout(xaxis=x_axis_dict,
                     yaxis=y_axis_dict,
                     autosize=True,
                     margin=dict(autoexpand=True,l=100,r=20,t=110,),
                     showlegend=True
                     )
    annotations = []
    
    annotations.append(dict(xref='paper',yref='paper',x=0.0,y=1.05,xanchor='left',yanchor='bottom',
                           text = title,
                           font = dict(family='Arial',size=16,color='rgb(37,37,37)'),showarrow=False))
    
    if(len(additional_annotations) > 0):
        annotations.append(additional_annotations)
    
    fig.update_layout(annotations=annotations)
    
    return fig

confirmed_agg_ts = confirmed_df.copy().drop(['Lat','Long','Country/Region','Province/State'],axis=1).sum()
death_agg_ts = deaths_df.copy().drop(['Lat','Long','Country/Region','Province/State'],axis=1).sum()
recovered_agg_ts = recovery_df.copy().drop(['Lat','Long','Country/Region','Province/State'],axis=1).sum()

active_agg_ts = pd.Series(
    data=np.array([x1-x2-x3 for (x1,x2,x3) in zip(confirmed_agg_ts.values,death_agg_ts.values,recovered_agg_ts.values)]),
    index=confirmed_agg_ts.index)

Covid-19 Case studies - Comparing the relationship between the number of confirmed, active, recovered and deaths due to Covid-19

ts_array = [confirmed_agg_ts, active_agg_ts, recovered_agg_ts, death_agg_ts]
labels = ['Confirmed', 'Active', 'Recovered', 'Deaths']
colors = [color_arr[5], color_arr[0], color_arr[2], color_arr[3]]
mode_size = [8,8,12,8]
line_size = [2,2,4,2]

fig2 = draw_plot(ts_array = ts_array,
                ts_label = labels,
                title = 'Covid-19 Case Status',
                colors = colors, mode_size = mode_size,
                line_size = line_size,
                x_axis_title = "Date",
                y_axis_title = "Case Count",
                tickangle = 315,
                y_axis_type = '', additional_annotations = [])
fig2.show()

cases_country_df.copy().drop(
['Lat','Long_','Last_Update','People_Tested','People_Hospitalized','UID', 'ISO3'],axis=1).sort_values(
'Confirmed',ascending=False).reset_index(drop=True).style.bar(align="left",width=98,color='#d65f5f')

	Country_Region	Confirmed	Deaths	Recovered	Active	Incident_Rate	Mortality_Rate
0	US	32744100.000000	582153.000000			9938.528368	1.777887
1	India	22992517.000000	249992.000000	19027304.000000	3715221.000000	1666.119126	1.087275
2	Brazil	15209990.000000	423229.000000	13481062.000000	1305699.000000	7155.641838	2.782573
3	France	5841593.000000	106845.000000	372400.000000	5362348.000000	8949.408146	1.829039
4	Turkey	5044936.000000	43311.000000	4743871.000000	257754.000000	5981.730863	0.858504
5	Russia	4832959.000000	111740.000000	4451443.000000	269776.000000	3311.732541	2.312041
6	United Kingdom	4452966.000000	127870.000000	15128.000000	4309968.000000	6559.475794	2.871569
7	Italy	4116287.000000	123031.000000	3619586.000000	373670.000000	6808.075667	2.988883
8	Spain	3581392.000000	78895.000000	150376.000000	3352121.000000	7659.947860	2.202914
9	Germany	3539590.000000	85120.000000	3207460.000000	247010.000000	4224.663806	2.404798
10	Argentina	3165121.000000	67821.000000	2837058.000000	260242.000000	7003.134386	2.142762
11	Colombia	3015301.000000	78342.000000	2835554.000000	101405.000000	5925.963237	2.598149
12	Poland	2835083.000000	70034.000000	2576034.000000	189015.000000	7490.983669	2.470263
13	Iran	2673219.000000	75261.000000	2127192.000000	470766.000000	3182.670575	2.815370
14	Mexico	2366496.000000	219089.000000	1888638.000000	258769.000000	1851.830086	9.257949
15	Ukraine	2180429.000000	48544.000000	1828877.000000	303008.000000	4985.688516	2.226351
16	Peru	1850290.000000	64103.000000	1798844.000000	-12657.000000	5611.727047	3.464484
17	Indonesia	1718575.000000	47218.000000	1574615.000000	96742.000000	628.309538	2.747509
18	Czechia	1646981.000000	29749.000000	1583590.000000	33642.000000	15379.435692	1.806275
19	South Africa	1597724.000000	54825.000000	1517350.000000	25549.000000	2693.912140	3.431444
20	Netherlands	1592742.000000	17602.000000	26310.000000	1548830.000000	9295.324220	1.105138
21	Canada	1302489.000000	24660.000000	1201850.000000	75979.000000	3440.667934	1.893298
22	Chile	1252808.000000	27318.000000	1188831.000000	36659.000000	6553.642514	2.180542
23	Iraq	1117627.000000	15800.000000	1011606.000000	90221.000000	2778.611266	1.413710
24	Philippines	1108826.000000	18562.000000	1030367.000000	59897.000000	1011.877187	1.674023
25	Romania	1066731.000000	29034.000000	1015092.000000	22605.000000	5545.007969	2.721773
26	Belgium	1017876.000000	24583.000000		993293.000000	8782.655094	2.415127
27	Sweden	1007792.000000	14173.000000		993619.000000	9978.859858	1.406342
28	Pakistan	864557.000000	19106.000000	766492.000000	78959.000000	391.392945	2.209918
29	Portugal	839740.000000	16993.000000	800645.000000	22102.000000	8235.403842	2.023603
30	Israel	838993.000000	6378.000000	831641.000000	974.000000	9693.131833	0.760197
31	Hungary	792879.000000	28792.000000	583802.000000	180285.000000	8207.559768	3.631323
32	Bangladesh	775027.000000	11972.000000	712277.000000	50778.000000	470.599249	1.544720
33	Jordan	720998.000000	9125.000000	705392.000000	6481.000000	7066.432490	1.265607
34	Serbia	701326.000000	6576.000000		694750.000000	8026.740312	0.937652
35	Switzerland	674296.000000	10716.000000	317600.000000	345980.000000	7791.169986	1.589213
36	Japan	650256.000000	10962.000000	559397.000000	79897.000000	514.132045	1.685798
37	Austria	631896.000000	10392.000000	605441.000000	16063.000000	7016.077456	1.644574
38	United Arab Emirates	537524.000000	1615.000000	517805.000000	18104.000000	5434.805468	0.300452
39	Lebanon	533141.000000	7507.000000	487126.000000	38508.000000	7811.083883	1.408070
40	Morocco	513922.000000	9077.000000	501146.000000	3699.000000	1392.344163	1.766221
41	Malaysia	444484.000000	1700.000000	405388.000000	37396.000000	1373.305405	0.382466
42	Saudi Arabia	427370.000000	7085.000000	410816.000000	9469.000000	1227.585548	1.657814
43	Bulgaria	411280.000000	17045.000000	351397.000000	42838.000000	5919.022170	4.144379
44	Nepal	403794.000000	3859.000000	306794.000000	93141.000000	1385.855307	0.955685
45	Ecuador	402060.000000	19242.000000	342878.000000	39940.000000	2278.856389	4.785853
46	Kazakhstan	400068.000000	3377.000000	354189.000000	42502.000000	2130.661143	0.844107
47	Slovakia	385786.000000	12051.000000	255300.000000	118435.000000	7066.139673	3.123753
48	Belarus	369767.000000	2642.000000	360255.000000	6870.000000	3913.159475	0.714504
49	Panama	367908.000000	6277.000000	357353.000000	4278.000000	8526.715689	1.706133
50	Greece	363904.000000	11089.000000	93764.000000	259051.000000	3491.336898	3.047232
51	Croatia	344747.000000	7503.000000	328993.000000	8251.000000	8397.673428	2.176379
52	Azerbaijan	327087.000000	4698.000000	307011.000000	15378.000000	3225.972527	1.436315
53	Georgia	324256.000000	4336.000000	303679.000000	16241.000000	8128.397476	1.337215
54	Tunisia	321837.000000	11468.000000	279634.000000	30735.000000	2723.135649	3.563294
55	Bolivia	318610.000000	13228.000000	261552.000000	43830.000000	2729.454369	4.151784
56	West Bank and Gaza	302249.000000	3378.000000	287206.000000	11665.000000	5924.805975	1.117622
57	Paraguay	299684.000000	7209.000000	248587.000000	43888.000000	4201.650747	2.405534
58	Kuwait	286046.000000	1652.000000	270883.000000	13511.000000	6698.086412	0.577529
59	Dominican Republic	272108.000000	3540.000000	231963.000000	36605.000000	2508.392405	1.300954
60	Costa Rica	271478.000000	3430.000000	214788.000000	53260.000000	5329.248619	1.263454
61	Ethiopia	263120.000000	3897.000000	211493.000000	47730.000000	228.872477	1.481073
62	Denmark	260687.000000	2498.000000	246862.000000	11327.000000	4500.653724	0.958237
63	Lithuania	259862.000000	4053.000000	234223.000000	21586.000000	9545.709845	1.559674
64	Ireland	253189.000000	4921.000000	23364.000000	224904.000000	5127.571086	1.943607
65	Moldova	252798.000000	5958.000000	243731.000000	3109.000000	6266.740672	2.356822
66	Slovenia	246231.000000	4299.000000	232983.000000	8949.000000	11844.110341	1.745922
67	Egypt	238560.000000	13972.000000	177440.000000	47148.000000	233.118084	5.856808
68	Guatemala	235304.000000	7736.000000	214601.000000	12967.000000	1313.405264	3.287662
69	Uruguay	222870.000000	3171.000000	194964.000000	24735.000000	6415.875514	1.422803
70	Honduras	220988.000000	5701.000000	81639.000000	133648.000000	2231.163515	2.579778
71	Armenia	219596.000000	4256.000000	205675.000000	9665.000000	7410.687107	1.938105
72	Qatar	211389.000000	512.000000	202552.000000	8325.000000	7337.195338	0.242207
73	Venezuela	209162.000000	2304.000000	192075.000000	14783.000000	735.554998	1.101539
74	Oman	202137.000000	2138.000000	185607.000000	14392.000000	3958.330967	1.057698
75	Bosnia and Herzegovina	201217.000000	8862.000000	166047.000000	26308.000000	6133.140698	4.404200
76	Bahrain	191018.000000	691.000000	176338.000000	13989.000000	11225.899648	0.361746
77	Libya	180226.000000	3072.000000	166680.000000	10474.000000	2622.885640	1.704527
78	Nigeria	165468.000000	2065.000000	156318.000000	7085.000000	80.269880	1.247975
79	Kenya	163620.000000	2907.000000	112298.000000	48415.000000	304.288719	1.776678
80	North Macedonia	154051.000000	5109.000000	138370.000000	10572.000000	7394.282368	3.316434
81	Burma	142963.000000	3210.000000	132045.000000	7708.000000	262.752327	2.245336
82	Albania	131753.000000	2416.000000	118041.000000	11296.000000	4578.254222	1.833734
83	Sri Lanka	128530.000000	827.000000	105611.000000	22092.000000	600.235835	0.643430
84	Korea, South	128283.000000	1879.000000	118717.000000	7687.000000	250.214637	1.464730
85	Estonia	125696.000000	1206.000000	116648.000000	7842.000000	9475.484701	0.959458
86	Latvia	124301.000000	2213.000000	113214.000000	8874.000000	6590.015279	1.780356
87	Algeria	124288.000000	3335.000000	86554.000000	34399.000000	283.432255	2.683284
88	Cuba	117097.000000	741.000000	110312.000000	6044.000000	1033.821576	0.632809
89	Norway	117022.000000	767.000000	17998.000000	98257.000000	2158.582849	0.655432
90	Kosovo	105784.000000	2207.000000	96841.000000	6736.000000	5843.238329	2.086327
91	China	102643.000000	4846.000000	97390.000000	407.000000	7.307235	4.721218
92	Kyrgyzstan	99033.000000	1667.000000	92120.000000	5246.000000	1517.935327	1.683277
93	Montenegro	98365.000000	1544.000000	95070.000000	1751.000000	15661.670345	1.569664
94	Uzbekistan	95072.000000	664.000000	90664.000000	3744.000000	284.058187	0.698418
95	Ghana	93011.000000	783.000000	90697.000000	1531.000000	299.331138	0.841836
96	Zambia	92112.000000	1257.000000	90501.000000	354.000000	501.045586	1.364643
97	Finland	88866.000000	922.000000	46000.000000	41944.000000	1603.871556	1.037517
98	Thailand	86924.000000	452.000000	26873.000000	59599.000000	124.532991	0.519994
99	Cameroon	74946.000000	1152.000000	35261.000000	38533.000000	282.326467	1.537107
100	El Salvador	70380.000000	2164.000000	65921.000000	2295.000000	1085.072757	3.074737
101	Mozambique	70283.000000	825.000000	67799.000000	1659.000000	224.866491	1.173826
102	Cyprus	69708.000000	337.000000	39061.000000	30310.000000	5773.583874	0.483445
103	Luxembourg	68431.000000	804.000000	65460.000000	2167.000000	10931.888763	1.174906
104	Afghanistan	62063.000000	2698.000000	54382.000000	4983.000000	159.428834	4.347196
105	Singapore	61378.000000	31.000000	60953.000000	394.000000	1049.135068	0.050507
106	Namibia	50209.000000	691.000000	47384.000000	2134.000000	1976.019672	1.376247
107	Botswana	49041.000000	751.000000	46290.000000	2000.000000	2085.409026	1.531372
108	Jamaica	46782.000000	809.000000	22395.000000	23578.000000	1579.853308	1.729298
109	Cote d'Ivoire	46443.000000	291.000000	45874.000000	278.000000	176.065342	0.626575
110	Mongolia	45936.000000	179.000000	34230.000000	11527.000000	1401.217463	0.389673
111	Uganda	42384.000000	346.000000	41971.000000	67.000000	92.660851	0.816346
112	Senegal	40714.000000	1120.000000	39410.000000	184.000000	243.156774	2.750896
113	Madagascar	39162.000000	729.000000	36255.000000	2178.000000	141.424915	1.861498
114	Zimbabwe	38433.000000	1576.000000	36208.000000	649.000000	258.582983	4.100643
115	Maldives	37019.000000	87.000000	26547.000000	10385.000000	6848.496509	0.235014
116	Sudan	34889.000000	2446.000000	27949.000000	4494.000000	79.565751	7.010806
117	Malawi	34180.000000	1153.000000	32164.000000	863.000000	178.672663	3.373318
118	Malta	30458.000000	417.000000	29828.000000	213.000000	6898.144898	1.369098
119	Congo (Kinshasa)	30350.000000	772.000000	26434.000000	3144.000000	33.887365	2.543657
120	Australia	29938.000000	910.000000	23460.000000	5568.000000	117.589760	3.039615
121	Angola	28875.000000	636.000000	24772.000000	3467.000000	87.856035	2.202597
122	Cabo Verde	26578.000000	235.000000	23402.000000	2941.000000	4780.319000	0.884190
123	Rwanda	25714.000000	338.000000	24155.000000	1221.000000	198.529842	1.314459
124	Gabon	23565.000000	143.000000	20051.000000	3371.000000	1058.754709	0.606832
125	Syria	23439.000000	1664.000000	19024.000000	2751.000000	133.932115	7.099279
126	Guinea	22633.000000	150.000000	20231.000000	2252.000000	172.339591	0.662749
127	Cambodia	20223.000000	131.000000	8170.000000	11922.000000	120.958401	0.647777
128	Mauritania	18691.000000	456.000000	17918.000000	317.000000	401.986382	2.439677
129	Eswatini	18482.000000	671.000000	17784.000000	27.000000	1593.050638	3.630559
130	Guyana	14442.000000	327.000000	12267.000000	1848.000000	1836.098754	2.264229
131	Somalia	14415.000000	747.000000	6191.000000	7477.000000	90.699059	5.182102
132	Mali	14115.000000	502.000000	9039.000000	4574.000000	69.700833	3.556500
133	Trinidad and Tobago	13454.000000	215.000000	9381.000000	3858.000000	961.349519	1.598038
134	Andorra	13429.000000	127.000000	13021.000000	281.000000	17380.443927	0.945714
135	Burkina Faso	13382.000000	162.000000	13164.000000	56.000000	64.018667	1.210581
136	Tajikistan	13308.000000	90.000000	13218.000000	0.000000	139.531343	0.676285
137	Togo	13167.000000	125.000000	11694.000000	1348.000000	159.045999	0.949343
138	Haiti	13164.000000	263.000000	12304.000000	597.000000	115.448032	1.997873
139	Belize	12700.000000	323.000000	12303.000000	74.000000	3193.996293	2.543307
140	Papua New Guinea	12086.000000	121.000000	10599.000000	1366.000000	135.083978	1.001158
141	Djibouti	11355.000000	149.000000	11132.000000	74.000000	1149.289171	1.312197
142	Congo (Brazzaville)	11343.000000	148.000000	8208.000000	2987.000000	205.560183	1.304769
143	Suriname	11213.000000	218.000000	9831.000000	1164.000000	1911.413249	1.944172
144	Bahamas	10908.000000	214.000000	9854.000000	840.000000	2773.822117	1.961863
145	Lesotho	10773.000000	319.000000	6427.000000	4027.000000	502.882014	2.961106
146	South Sudan	10641.000000	115.000000	10462.000000	64.000000	95.062155	1.080725
147	Seychelles	8172.000000	28.000000	5658.000000	2486.000000	8309.945088	0.342633
148	Benin	7884.000000	100.000000	7652.000000	132.000000	65.032345	1.268392
149	Equatorial Guinea	7694.000000	112.000000	7279.000000	303.000000	548.402157	1.455680
150	Nicaragua	6989.000000	183.000000	4225.000000	2581.000000	105.501442	2.618400
151	Central African Republic	6674.000000	93.000000	5112.000000	1469.000000	138.184806	1.393467
152	Iceland	6519.000000	29.000000	6390.000000	100.000000	1910.329670	0.444854
153	Yemen	6485.000000	1275.000000	3001.000000	2209.000000	21.742798	19.660756
154	Gambia	5929.000000	175.000000	5598.000000	156.000000	245.338202	2.951594
155	Niger	5322.000000	192.000000	4904.000000	226.000000	21.985707	3.607666
156	San Marino	5083.000000	90.000000	4968.000000	25.000000	14977.311568	1.770608
157	Chad	4882.000000	171.000000	4657.000000	54.000000	29.721429	3.502663
158	Saint Lucia	4690.000000	75.000000	4461.000000	154.000000	2554.062811	1.599147
159	Burundi	4200.000000	6.000000	773.000000	3421.000000	35.321481	0.142857
160	Sierra Leone	4089.000000	79.000000	3093.000000	917.000000	51.259969	1.932013
161	Barbados	3946.000000	45.000000	3861.000000	40.000000	1373.137860	1.140395
162	Comoros	3860.000000	146.000000	3688.000000	26.000000	443.884797	3.782383
163	Eritrea	3754.000000	12.000000	3613.000000	129.000000	105.853018	0.319659
164	Guinea-Bissau	3741.000000	67.000000	3400.000000	274.000000	190.091657	1.790965
165	Vietnam	3507.000000	35.000000	2618.000000	854.000000	3.602888	0.998004
166	Timor-Leste	3353.000000	5.000000	1683.000000	1665.000000	254.315321	0.149120
167	Liechtenstein	2974.000000	58.000000	2879.000000	37.000000	7798.201222	1.950235
168	New Zealand	2643.000000	26.000000	2591.000000	26.000000	54.808633	0.983731
169	Monaco	2481.000000	32.000000	2420.000000	29.000000	6321.985526	1.289802
170	Sao Tome and Principe	2318.000000	35.000000	2262.000000	21.000000	1057.669932	1.509922
171	Liberia	2114.000000	85.000000	1962.000000	67.000000	41.797845	4.020814
172	Saint Vincent and the Grenadines	1922.000000	12.000000	1741.000000	169.000000	1732.358694	0.624350
173	Laos	1327.000000	1.000000	238.000000	1088.000000	18.239156	0.075358
174	Mauritius	1256.000000	17.000000	1124.000000	115.000000	98.760229	1.353503
175	Bhutan	1241.000000	1.000000	1089.000000	151.000000	160.832128	0.080580
176	Antigua and Barbuda	1237.000000	32.000000	1170.000000	35.000000	1263.172943	2.586904
177	Taiwan*	1199.000000	12.000000	1089.000000	98.000000	5.034267	1.000834
178	Diamond Princess	712.000000	13.000000	699.000000	0.000000		1.825843
179	Tanzania	509.000000	21.000000	183.000000	305.000000	0.852108	4.125737
180	Brunei	230.000000	3.000000	218.000000	9.000000	52.573471	1.304348
181	Dominica	175.000000	0.000000	175.000000	0.000000	243.085941	0.000000
182	Grenada	160.000000	1.000000	158.000000	1.000000	142.198207	0.625000
183	Fiji	152.000000	3.000000	101.000000	48.000000	16.955883	1.973684
184	Saint Kitts and Nevis	45.000000	0.000000	44.000000	1.000000	84.599188	0.000000
185	Holy See	27.000000	0.000000	15.000000	12.000000	3337.453646	0.000000
186	Solomon Islands	20.000000	0.000000	20.000000	0.000000	3.063453	0.000000
187	MS Zaandam	9.000000	2.000000	7.000000	0.000000		22.222222
188	Vanuatu	4.000000	1.000000	3.000000	0.000000	1.366680	25.000000
189	Marshall Islands	4.000000	0.000000	4.000000	0.000000	6.847791	0.000000
190	Samoa	3.000000	0.000000	2.000000	1.000000	1.529598	0.000000
191	Micronesia	1.000000	0.000000	1.000000	0.000000	0.878619	0.000000

The columns of the cumulative collection of country dataframe

print(country_cumulative_df.columns)

Index(['Name', 'WHO Region', 'Cases - cumulative total',
       'Cases - cumulative total per 100000 population',
       'Cases - newly reported in last 7 days',
       'Cases - newly reported in last 7 days per 100000 population',
       'Cases - newly reported in last 24 hours', 'Deaths - cumulative total',
       'Deaths - cumulative total per 100000 population',
       'Deaths - newly reported in last 7 days',
       'Deaths - newly reported in last 7 days per 100000 population',
       'Deaths - newly reported in last 24 hours',
       'Transmission Classification'],
      dtype='object')

Refining the dataframe

country_cumulative_df = country_cumulative_df.drop(['WHO Region','Cases - cumulative total per 100000 population','Cases - newly reported in last 7 days per 100000 population','Deaths - cumulative total per 100000 population','Deaths - newly reported in last 7 days','Deaths - newly reported in last 7 days per 100000 population','Transmission Classification'],axis=1)
country_cumulative_df = country_cumulative_df.set_index('Name')
country_cumulative_df['Recovered'] = country_cumulative_df['Cases - cumulative total'] - country_cumulative_df['Deaths - cumulative total']
country_cumulative_df = country_cumulative_df.drop(['Cases - cumulative total', 'Deaths - newly reported in last 24 hours'], axis=1)

Countries available in our dataset

print("Countries available in the Dataset")
country_cumulative_df.copy().drop(
['Cases - newly reported in last 7 days','Cases - newly reported in last 24 hours','Deaths - cumulative total','Recovered'],axis=1).sort_values('Name',ascending=True).reset_index(drop=False).style.bar(align="left",width=80)

Countries available in the Dataset

	Name
0	Afghanistan
1	Albania
2	Algeria
3	American Samoa
4	Andorra
5	Angola
6	Anguilla
7	Antigua and Barbuda
8	Argentina
9	Armenia
10	Aruba
11	Australia
12	Austria
13	Azerbaijan
14	Bahamas
15	Bahrain
16	Bangladesh
17	Barbados
18	Belarus
19	Belgium
20	Belize
21	Benin
22	Bermuda
23	Bhutan
24	Bolivia (Plurinational State of)
25	Bonaire
26	Bosnia and Herzegovina
27	Botswana
28	Brazil
29	British Virgin Islands
30	Brunei Darussalam
31	Bulgaria
32	Burkina Faso
33	Burundi
34	Cabo Verde
35	Cambodia
36	Cameroon
37	Canada
38	Cayman Islands
39	Central African Republic
40	Chad
41	Chile
42	China
43	Colombia
44	Comoros
45	Congo
46	Cook Islands
47	Costa Rica
48	Croatia
49	Cuba
50	Curaçao
51	Cyprus
52	Czechia
53	Côte d’Ivoire
54	Democratic People's Republic of Korea
55	Democratic Republic of the Congo
56	Denmark
57	Djibouti
58	Dominica
59	Dominican Republic
60	Ecuador
61	Egypt
62	El Salvador
63	Equatorial Guinea
64	Eritrea
65	Estonia
66	Eswatini
67	Ethiopia
68	Falkland Islands (Malvinas)
69	Faroe Islands
70	Fiji
71	Finland
72	France
73	French Guiana
74	French Polynesia
75	Gabon
76	Gambia
77	Georgia
78	Germany
79	Ghana
80	Gibraltar
81	Global
82	Greece
83	Greenland
84	Grenada
85	Guadeloupe
86	Guam
87	Guatemala
88	Guernsey
89	Guinea
90	Guinea-Bissau
91	Guyana
92	Haiti
93	Holy See
94	Honduras
95	Hungary
96	Iceland
97	India
98	Indonesia
99	Iran (Islamic Republic of)
100	Iraq
101	Ireland
102	Isle of Man
103	Israel
104	Italy
105	Jamaica
106	Japan
107	Jersey
108	Jordan
109	Kazakhstan
110	Kenya
111	Kiribati
112	Kosovo[1]
113	Kuwait
114	Kyrgyzstan
115	Lao People's Democratic Republic
116	Latvia
117	Lebanon
118	Lesotho
119	Liberia
120	Libya
121	Liechtenstein
122	Lithuania
123	Luxembourg
124	Madagascar
125	Malawi
126	Malaysia
127	Maldives
128	Mali
129	Malta
130	Marshall Islands
131	Martinique
132	Mauritania
133	Mauritius
134	Mayotte
135	Mexico
136	Micronesia (Federated States of)
137	Monaco
138	Mongolia
139	Montenegro
140	Montserrat
141	Morocco
142	Mozambique
143	Myanmar
144	Namibia
145	Nauru
146	Nepal
147	Netherlands
148	New Caledonia
149	New Zealand
150	Nicaragua
151	Niger
152	Nigeria
153	Niue
154	North Macedonia
155	Northern Mariana Islands (Commonwealth of the)
156	Norway
157	Oman
158	Other
159	Pakistan
160	Palau
161	Panama
162	Papua New Guinea
163	Paraguay
164	Peru
165	Philippines
166	Pitcairn Islands
167	Poland
168	Portugal
169	Puerto Rico
170	Qatar
171	Republic of Korea
172	Republic of Moldova
173	Romania
174	Russian Federation
175	Rwanda
176	Réunion
177	Saba
178	Saint Barthélemy
179	Saint Helena
180	Saint Kitts and Nevis
181	Saint Lucia
182	Saint Martin
183	Saint Pierre and Miquelon
184	Saint Vincent and the Grenadines
185	Samoa
186	San Marino
187	Sao Tome and Principe
188	Saudi Arabia
189	Senegal
190	Serbia
191	Seychelles
192	Sierra Leone
193	Singapore
194	Sint Eustatius
195	Sint Maarten
196	Slovakia
197	Slovenia
198	Solomon Islands
199	Somalia
200	South Africa
201	South Sudan
202	Spain
203	Sri Lanka
204	Sudan
205	Suriname
206	Sweden
207	Switzerland
208	Syrian Arab Republic
209	Tajikistan
210	Thailand
211	The United Kingdom
212	Timor-Leste
213	Togo
214	Tokelau
215	Tonga
216	Trinidad and Tobago
217	Tunisia
218	Turkey
219	Turkmenistan
220	Turks and Caicos Islands
221	Tuvalu
222	Uganda
223	Ukraine
224	United Arab Emirates
225	United Republic of Tanzania
226	United States Virgin Islands
227	United States of America
228	Uruguay
229	Uzbekistan
230	Vanuatu
231	Venezuela (Bolivarian Republic of)
232	Viet Nam
233	Wallis and Futuna
234	Yemen
235	Zambia
236	Zimbabwe
237	occupied Palestinian territory, including east Jerusalem

Getting the country name from the user

country = input("Enter country name : ").title()
is_country_exist = True
if 'Of' in country:
    country = country.replace('Of','of')
try:
    country_cumulative_df.loc[[country]]
except:
    print("Sorry, country not found!")
    is_country_exist = False

Enter country name : india

Visualization for the number of recovered, deaths, newly reported in last 24 hours and newly reported in last 7 days due to Covid-19

if(is_country_exist):
    recovered = country_cumulative_df.at[country,'Recovered']
    deaths = country_cumulative_df.at[country,'Deaths - cumulative total']
    new24 = country_cumulative_df.at[country, 'Cases - newly reported in last 24 hours']
    new7 = country_cumulative_df.at[country, 'Cases - newly reported in last 7 days']
    fig3 = plt.figure(figsize=(8,8))
    font = {'family': 'Arial',
            'weight': 'bold',
            'size'  : 12
           }
    plt.rc('font', **font)
    plt.rcParams.update({'text.color' : "black", 'axes.labelcolor' : "black"})
    plt.pie([recovered, deaths, new24, new7],
            labels = ['Recovered','Deaths','New cases - 24hrs', 'New cases - 7days'],
            colors = ['lightgreen','red','pink','orange'],
            explode = [0.2,0.02,0.2,0.1],
            startangle = 180,
            autopct = '%1.1f%%'
           )
    plt.title(country)
    plt.legend()
    plt.show()
else:
    print("Please enter a valid country name!")